






'''simple docstring'''





# Lint as: python3
import itertools
import os
import re


_UpperCamelCase						:						Optional[Any]  =					re.compile(R'([A-Z]+)([A-Z][a-z])')
_UpperCamelCase						:						List[str]  =					re.compile(R'([a-z\d])([A-Z])')

_UpperCamelCase						:						Union[str, Any]  =					re.compile(R'(?<!_)_(?!_)')
_UpperCamelCase						:						Any  =					re.compile(R'(_{2,})')

_UpperCamelCase						:						List[str]  =					R'^\w+(\.\w+)*$'

_UpperCamelCase						:						Tuple  =					R'<>:/\|?*'
def __UpperCAmelCase   (					A					:			Optional[int]		)    ->   Any:
			UpperCAmelCase_      :       Optional[Any]          =       _uppercase_uppercase_re.sub(r'''\1_\2'''			,       A		)
			UpperCAmelCase_      :       Tuple          =       _lowercase_uppercase_re.sub(r'''\1_\2'''			,       A		)
			return name.lower()
def __UpperCAmelCase   (					A					:			List[str]		)    ->   Any:
			UpperCAmelCase_      :       List[Any]          =       _single_underscore_re.split(A		)
			UpperCAmelCase_      :       Union[str, Any]          =       [_multiple_underscores_re.split(A		) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(A		) if n != ''''''		)
def __UpperCAmelCase   (					A					:			str		)    ->   List[Any]:
			if os.path.basename(A		) != name:
						raise ValueError(F"Should be a dataset name, not a path: {name}"		)
			return camelcase_to_snakecase(A		)
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Any		)    ->   str:
			if os.path.basename(A		) != name:
						raise ValueError(F"Should be a dataset name, not a path: {name}"		)
			if not re.match(_split_re			,       A		):
						raise ValueError(F"Split name should match '{_split_re}'' but got '{split}'."		)
			return F"{filename_prefix_for_name(A		)}-{split}"
def __UpperCAmelCase   (					A					:			int			,       A					:			Union[str, Any]			,       A					:			Any			,       A					:			Dict=None		)    ->   Dict:
			UpperCAmelCase_      :       Union[str, Any]          =       filename_prefix_for_split(A			,       A		)
			if filetype_suffix:
						prefix += F".{filetype_suffix}"
			UpperCAmelCase_      :       Dict          =       os.path.join(A			,       A		)
			return F"{filepath}*"







def __UpperCAmelCase   (					A					:			List[str]			,       A					:			Dict			,       A					:			Optional[int]			,       A					:			List[str]=None			,       A					:			Dict=None		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Dict          =       filename_prefix_for_split(A			,       A		)
			UpperCAmelCase_      :       Dict          =       os.path.join(A			,       A		)

			if shard_lengths:
						UpperCAmelCase_      :       Union[str, Any]          =       len(A		)
						UpperCAmelCase_      :       Tuple          =       [F"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(A		)]
						if filetype_suffix:
									UpperCAmelCase_      :       str          =       [filename + F".{filetype_suffix}" for filename in filenames]
						return filenames
			else:
						UpperCAmelCase_      :       Any          =       prefix
						if filetype_suffix:
									filename += F".{filetype_suffix}"
						return [filename]










'''simple docstring'''





from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Optional[int]		)    ->   str:
			UpperCAmelCase_      :       List[Any]          =       []
			for part_id in partition_order:
						UpperCAmelCase_      :       Any          =       df.where(F"SPARK_PARTITION_ID() = {part_id}"		).collect()
						for row_idx, row in enumerate(A		):
									expected_row_ids_and_row_dicts.append((F"{part_id}_{row_idx}", row.asDict())		)
			return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Any:
			UpperCAmelCase_      :       Any          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[str]          =       spark.range(1_0_0		).repartition(1		)
			UpperCAmelCase_      :       List[Any]          =       Spark(A		)
			# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
			# that each partition can hold 2 rows.
			spark_builder._repartition_df_if_needed(max_shard_size=1_6		)
			# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
			assert spark_builder.df.rdd.getNumPartitions() == 5_0
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   str:
			UpperCAmelCase_      :       List[str]          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       Optional[Any]          =       spark.range(1_0		).repartition(2		)
			UpperCAmelCase_      :       int          =       [1, 0]
			UpperCAmelCase_      :       str          =       _generate_iterable_examples(A			,       A		)  # Reverse the partitions.
			UpperCAmelCase_      :       Optional[Any]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       A		)

			for i, (row_id, row_dict) in enumerate(generate_fn()		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       expected_row_ids_and_row_dicts[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       str          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[Any]          =       spark.range(1_0		).repartition(1		)
			UpperCAmelCase_      :       Optional[int]          =       SparkExamplesIterable(A		)
			assert it.n_shards == 1
			for i, (row_id, row_dict) in enumerate(A		):
						assert row_id == F"0_{i}"
						assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Dict          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       Dict          =       spark.range(3_0		).repartition(3		)
			# Mock the generator so that shuffle reverses the partition indices.
			with patch('''numpy.random.Generator'''		) as generator_mock:
						UpperCAmelCase_      :       Any          =       lambda A		: x.reverse()
						UpperCAmelCase_      :       Dict          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [2, 1, 0]		)

						UpperCAmelCase_      :       List[Any]          =       SparkExamplesIterable(A		).shuffle_data_sources(A		)
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(A		):
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       expected_row_ids_and_row_dicts[i]
									assert row_id == expected_row_id
									assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   int:
			UpperCAmelCase_      :       Tuple          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       int          =       spark.range(2_0		).repartition(4		)

			# Partitions 0 and 2
			UpperCAmelCase_      :       int          =       SparkExamplesIterable(A		).shard_data_sources(worker_id=0			,       num_workers=2		)
			assert shard_it_a.n_shards == 2
			UpperCAmelCase_      :       Union[str, Any]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [0, 2]		)
			for i, (row_id, row_dict) in enumerate(A		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       expected_row_ids_and_row_dicts_a[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict

			# Partitions 1 and 3
			UpperCAmelCase_      :       Tuple          =       SparkExamplesIterable(A		).shard_data_sources(worker_id=1			,       num_workers=2		)
			assert shard_it_a.n_shards == 2
			UpperCAmelCase_      :       List[str]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [1, 3]		)
			for i, (row_id, row_dict) in enumerate(A		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       expected_row_ids_and_row_dicts_a[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Any:
			UpperCAmelCase_      :       List[Any]          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[str]          =       spark.range(1_0_0		).repartition(1		)
			UpperCAmelCase_      :       int          =       Spark(A		)
			# Choose a small max_shard_size for maximum partitioning.
			spark_builder._repartition_df_if_needed(max_shard_size=1		)
			# The new number of partitions should not be greater than the number of rows.
			assert spark_builder.df.rdd.getNumPartitions() == 1_0_0


